Analysis method of lane stripe images, image analysis device, and non-transitory computer readable medium thereof

ABSTRACT

An analysis method of lane stripe images, an image analysis device and a non-transitory computer readable medium thereof are provided to perform steps of: setting a reference point as a center to recognize the lane stripe image in a plurality of default directions; defining a plurality of preset sections onto the lane stripe image and determining a characteristic value of the lane stripe image in each of the preset sections whenever the lane stripe image is recognized in one of the default directions; determining a first feature parameter according to the characteristic values of the lane stripe image in the preset sections when the lane stripe image is recognized in at least one of the default directions; and determining an actual lane parameter of the lane stripe image according to at least the first feature parameter.

CROSS-REFERENCE TO RELATED APPLICATIONS

This non-provisional application claims priority under 35 U.S.C. §119(a) on Patent Application No(s). 105117239 filed in Taiwan, R.O.C. onJun. 1, 2016, the entire contents of which are hereby incorporated byreference.

BACKGROUND

Technical Field

The disclosure relates to an analysis method of lane stripe images, animage analysis device and a non-transitory computer readable mediumthereof, more particularly to an image analysis device, an analysismethod and a non-transitory computer readable medium thereof for sortinglane stripe images.

Related Art

Recently, automatic driving technology has widely been paid attentionto, and more and more automotive manufacturers and enterprises endeavorto develop it. The core of the automatic driving technology is based onadvanced driving assistance systems (ADAS) and includes lane departurewarning, collision warning, automate braking and so on.

However, the automatic driving technology must detect real-time trafficstates anytime for the real-time determination of driving. Moreover,various traffic states lead to the difficulty in automate driving. Toachieve a variety of requirements of the automatic driving technologyand the sufficient speeds of calculation and recognition, automaticdriving assistance systems need high standard performance for thedetermination of automate driving and the complicated data process. Thatis why the cost of an automatic driving assistance system can not bereduced.

SUMMARY

According to one or more embodiments, an analysis method of lane stripeimages includes the following steps: setting a reference point as acenter to recognize the lane stripe image in a plurality of defaultdirections; defining a plurality of preset sections, which is arrangedin parallel in a reference line vertical to the default direction, ontothe lane stripe image whenever the lane stripe image is recognized inone of the default directions; determining a characteristic value of thelane stripe image in each of the preset sections; determining a firstfeature parameter according to the characteristic values of the lanestripe image in the preset sections when the lane stripe image isrecognized in at least one of the default directions; and determining atleast one actual lane parameter of the lane stripe image related to anenvironmental message of one or more real lanes according to at leastthe first feature parameter.

According to one or more embodiments, an image analysis device includesa projection computing module, a processing module and a determinationmodule. The projection computing module is capable of setting areference point as a center to recognize a lane stripe image in aplurality of default directions. Whenever the lane stripe image isrecognized in one of the default directions, a plurality of presetsections is defined onto the lane stripe image, and a characteristicvalue of the lane stripe image in each of the preset sections isdetermined. The preset sections are arranged in parallel in a referenceline vertical to the default direction. The processing module iselectrically connected to the projection computing module and is capableof determining a first feature parameter according to the characteristicvalues of the lane stripe image in the preset sections when the lanestripe image is recognized in at least one of the default directions.The determination module is electrically connected to the processingmodule and is capable of determining at least one actual lane parameterof the lane stripe image according to at least the first featureparameter. The actual lane parameter is related to an environmentalmessage of one or more real lanes.

According to one or more embodiments, a non-transitory computer readablemedium is loaded in and executable to an electronic device that includesa projection computing module, a processing module and a determinationmodule, to perform the following steps: driving the projection computingmodule to set a reference point as a center to recognize a lane stripeimage in a plurality of default directions; driving the projectioncomputing module to define a plurality of preset sections, which isarranged in parallel in a reference line vertical to the defaultdirection, onto the lane stripe image and determine a characteristicvalue of the lane stripe image in each of the preset sections wheneverthe lane stripe image is recognized in one of the default directions;driving the processing module to determine a first feature parameterrelated to the characteristic values of the lane stripe image in thepreset sections when the lane stripe image is recognized in at least oneof the default directions; driving the determination module to determineat least one actual lane parameter of the lane stripe image, related toan environmental message of one or more real lanes, according to atleast the first feature parameter.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure will become more fully understood from thedetailed description given hereinbelow and the accompanying drawingswhich are given by way of illustration only and thus are not limitativeof the present disclosure and wherein:

FIG. 1A is a block diagram of an image analysis device according to anembodiment of the disclosure;

FIG. 1B is a schematic view of a lane stripe image according to anembodiment of the disclosure;

FIG. 2 is a block diagram of an image analysis device according toanother embodiment of the disclosure;

FIGS. 3A to 3E are schematic views of a roadway image and a lane stripeimage according to the embodiment in FIG. 2;

FIGS. 4A to 4C are schematic views of a lane stripe model according toanother embodiment of the disclosure;

FIG. 5 is a schematic view of the comparison between a roadway image anda lane stripe model according to another embodiment of the disclosure;

FIG. 6 is a block diagram of an image analysis device according to yetanother embodiment of the disclosure;

FIG. 7 is a flow chart of an analysis method of lane stripe imagesaccording to an embodiment of the disclosure; and

FIG. 8 is a flow chart of a non-transitory computer readable mediumexecutable to the image analysis device according to an embodiment ofthe disclosure.

DETAILED DESCRIPTION

In the following detailed description, for purposes of explanation,numerous specific details are set forth in order to provide a thoroughunderstanding of the disclosed embodiments. It will be apparent,however, that one or more embodiments may be practiced without thesespecific details. In other instances, well-known structures and devicesare schematically shown in order to simplify the drawings.

Please refer to FIG. 1A and FIG. 1B. FIG. 1A is a block diagram of animage analysis device according to an embodiment of the disclosure, andFIG. 1B is a schematic view of a lane stripe image according to anembodiment of the disclosure. As shown in the figures, an image analysisdevice 10 includes a projection computing module 11, a processing module12 and a determination module 13. The processing module 12 iselectrically connected to the projection computing module 11 and thedetermination module 13. The projection computing module 11 receives alane stripe image 15 and sets a reference point as a center in the lanestripe image 15 to recognize the lane stripe image 15 in a plurality ofdefault directions. In an embodiment, the manner used by the projectioncomputing module 11 to recognize the lane stripe image 15 exemplarilyincludes the following steps. The lane stripe image 15 is clockwiselyrotated by a variety of preset angles, which have an interval of 15°therebetween, from 0° to 90°. Whenever the lane stripe image 15 isrotated to one of the preset angles, the projection computing module 11defines a plurality of preset sections RG onto the lane stripe image 15and determines the characteristic value of the lane stripe image 15 ineach of the preset sections RG. The preset sections RG are arranged inparallel in a reference line Base vertical to a default direction L.Specifically, the projection computing module 11 defines the presetsections RG onto the lane stripe image 15 and determines thecharacteristic values of the lane stripe image 15 in the preset sectionsRG when the lane stripe image 15 is, for example, respectively andprogressively rotated to 15°, 30°, 45° and so on. In this embodiment,even though the projection computing module 11 has not been rotated thelane stripe image 15 yet (i.e. the preset angle is 0°), the projectioncomputing module 11 also defines the preset sections RG onto the lanestripe image 15 and determines the characteristic values of the lanestripe image 15 in the preset sections RG.

In practice, the reference line Base is, for example, a virtual lineparallel to an edge of the lane stripe image 15 when the lane stripeimage 15 has not been rotated, and the preset sections RG are, forexample, image sections arranged in parallel along the above edge of thelane stripe image 15. In another embodiment, recognizing a lane stripeimage in a plurality of default directions is rotating a reference lineand preset sections instead of rotating the lane stripe image.Particularly, the above reference line is clockwisely rotated to avariety of angles with an interval of 15° therebetween from 0° to 90°relative to a reference point as a center of rotation, and a pluralityof preset sections, which is arranged in parallel along the referenceline, is defined onto the lane stripe image, so as to determine thecharacteristic values of the lane stripe image in the preset sections.In other words, in this embodiment, whenever the reference line isrotated to one of the preset angles, the lane stripe image is recognizedin a direction vertical to the reference line. Then, a plurality ofpreset sections, which is arranged in parallel along the reference line,is defined onto the lane stripe image, and the characteristic value ofthe lane stripe image in each of the preset sections is determined. Fora concise description, the following embodiments are based on the caseof rotating lane stripe images, but are not used to limit the possibleimplementations of recognizing lane stripe images in the disclosure.

The characteristic value of the lane stripe image in each of the presetsections is, for example, the number of pixels occupied by a lanestripe, the number of units of block or other suitable characteristicvalue. For example, the reference line is parallel to a row of pixels inthe lane stripe image that has not been rotated yet, and a presetsection is a couple of columns of pixels in the lane stripe image thathas not been rotated yet. For example, the characteristic value of thelane stripe image in a preset section is the number of pixels, occupiedby a fraction of a lane stripe, in a column of pixels in the lane stripeimage. As another example, the preset sections are sections arranged inparallel along the reference line and have the same width. In each ofthe preset sections, there is a plurality of units of block, having thesame length and arranged in parallel along a direction vertical to thereference direction. The characteristic value of the lane stripe imagein each preset section is the number of units of block occupied by afraction of a lane stripe in each preset section in the lane stripeimage.

The processing module 12 determines a first feature parameter (referredto as test parameter) according to the characteristic values of the lanestripe image in the preset sections when the projection computing module11 recognizes the lane stripe image in at least one of the defaultdirections. Based on the previous example, the first feature parameterincludes, for example, the number of units of block occupied by the lanestripe in each of the preset sections when the lane stripe image has notbeen rotated, the number of units of block occupied by the lane stripein each of the preset sections when the lane stripe image is clockwiselyrotated by a 15° angle, the number of units of block occupied by thelane stripe in each of the preset sections when the lane stripe image isclockwisely rotated by a 30° angle, or the others that can be deduced byanalogy. Alternatively, the first feature parameter is, for example, thesum of the numbers of units of block occupied by the lane stripe in eachof the preset sections when the lane stripe image has not been rotatedand is rotated by an angle of 15°, 30°, 45° and so on. This embodimentis not limited to the number of first feature parameters. The processingmodule 12 may transform the characteristic values into a first featureparameter by other calculating manners, and this embodiment has nolimitation thereon.

The determination module 13 determines at least one actual laneparameter of the lane stripe image according to the first featureparameter. The actual lane parameter is related to an environmentalmessage of one or more real lanes, such as the width of the real lane,the curvature of the real lane, the actual distance between the leftlane stripe and the vehicle, a vehicle's front rake angle, the anglebetween the lane centerline and the vehicle centerline, or others. Whenthe determination module 13 determines the actual lane parameter of thelane stripe image according to the first feature parameter, the actuallane parameter can be considered the real situation of the lane whilethe lane stripe image is captured.

The determination module 13 determines the model category of the lanestripe image by, for example, the support vector machine (SVM), deeplearning or other suitable algorithms, and this embodiment is notlimited thereto.

Please refer to FIGS. 2 to 5. FIG. 2 is a block diagram of an imageanalysis device according to another embodiment of the disclosure, FIGS.3A to 3E are schematic views of a roadway image and a lane stripe imageaccording to the embodiment in FIG. 2, FIGS. 4A to 4C are schematicviews of a lane stripe model according to another embodiment of thedisclosure, and FIG. 5 is a schematic view of the comparison between aroadway image and a lane stripe model according to another embodiment ofthe disclosure. As shown in the figures, an image analysis device 20includes a projection computing module 21, a processing module 22, adetermination module 23, an image capturing module 24, a detectionmodule 25, and a noise processing module 26. The image capturing module24 is electrically connected to the detection module 25, the processingmodule 22 is electrically connected to the projection computing module21 and the determination module 23, the detection module 25 iselectrically connected to the image capturing module 24, the noiseprocessing module 26 and the determination module 23, and the noiseprocessing module 26 is electrically connected to the projectioncomputing module 21, the detection module 25 and the determinationmodule 23.

In this embodiment, the image capturing module 24 captures a roadwayimage 30. The image capturing module 24 is disposed on, for example, avehicle's windshield so that the image capturing module 24 can capture avariety of roadway images 30 with a drive. The detection module 25receives a variety of roadway images 30 captured by the image capturingmodule 24 and recognizes a first lane stripe L1 and a second lane stripeL2 in the roadway image 30. The detection module 25 determines areference point P according to the first lane stripe L1 and the secondlane stripe L2 in the roadway image 30.

For example, the first lane stripe L1 is a lane marking at the left sideof the vehicle on the lane or any possible boundary of the lane that canbe used to recognize the lane on which the vehicle goes, and the secondlane stripe L2 is a lane marking at the right side of the vehicle on thelane or any possible boundary of the lane that can be used to recognizethe lane on which the vehicle goes. In an embodiment, the referencepoint P is located in a tangential direction of the first lane stripe L1and a tangential direction of the second lane stripe L2. Particularly,the detection module 25 recognizes the first lane stripe L1 and thesecond lane stripe L2 in a peripheral region R of the roadway image 30and sets the intersection point of the extension of the first lanestripe L1 and the extension of the second lane stripe L2 to be thereference point P. In other words, the reference point P is, forexample, the vanish point of the roadway image 30, toward which theextension of the first lane stripe L1 and the extension of the secondlane stripe L2 are converged.

The noise processing module 26 receives the data about the referencepoint P recognized by the detection module 25, and generates and outputsa lane stripe image 31 to the projection computing module 21 byfiltering noises in the roadway image 30 according to the referencepoint P. For example, the noise processing module 26 performs gray orcolor gradient process onto the roadway image 30 or performs imagegradient process onto the roadway image 30 according to its ridge lineto form the image shown in FIG. 3B. Then, the noise processing module 26removes noises from the roadway image 30 according to the position orcoordinate of the reference point P. In an embodiment, the noiseprocessing module 26 removes noises above the reference point P in thedirection Y, as shown in FIG. 3C. The lane stripe image 31 generated byfiltering the roadway image 30 by the noise processing module 26 isoutputted to the projection computing module 21. In other words, thelane stripe image 31 is formed by pixels, which possibly present one ormore lane stripes and are selected from the roadway image 30 generatedby performing image process onto the roadway image 30 by the noiseprocessing module 26.

The projection computing module 21 receives the lane stripe image 31 androtates the lane stripe image 31 relative to the reference point P as acenter of rotation in the lane stripe image 31 to a plurality of presetangles. As shown in FIG. 3C, when the projection computing module 21 hasnot rotated the lane stripe image 31 yet, a plurality of preset sectionswould be defined onto the lane stripe image 31, so as to determine thecharacteristic values of the lane stripe image 31 in the presetsections. For the reference line B, the preset sections are arranged inparallel in the reference line B, and one of the preset sections, apreset section an, is shown as in FIG. 3C. The projection computingmodule 21 sets the number of units of block of the lane stripe of thelane stripe image 31 in each of the preset sections to be acharacteristic value, to constitute the distribution as shown in thelower part of FIG. 3C.

Then, as an example, the projection computing module 21 clockwiselyrotates the lane stripe image 31 by a variety of preset angles, whichhave an interval of 15° therebetween, from 0° to 90°, so as to determinethe characteristic values of the lane stripe image 31 in the presetsections, respectively. As shown in FIG. 3D, the lane stripe image 31 isclockwisely rotated by 60°, and a plurality of preset sections isdefined onto the lane stripe image 31, so as to respectively determinethe characteristic values of the lane stripe image 31 in the presetsections. In this embodiment, for a reference line B, the presetsections are arranged in parallel in the reference line B, as shown byone preset section bn in FIG. 3D. The projection computing module 21sets the number of units of block of the lane stripe of the 60°clockwisely-rotated lane stripe image 31 in the preset section bn to bea characteristic value, so as to constitute the distribution as shown inthe lower part of FIG. 3D. The processing module 22 determines a firstfeature parameter according to the characteristic values of the lanestripe image in the preset sections whenever the projection computingmodule 21 rotates the lane stripe image to one of the preset angles.

In an embodiment, as shown in FIG. 3E, the detection module 25 furtherdetermines the image center C of the lane stripe image 31 and determinesa second feature parameter (referred to as test parameter) according tothe displacement of the reference point P relative to the image centerC. For example, the second feature parameter is the displacement vectorof the reference point P relative to the image center C, or the secondfeature parameter includes the displacement of the X coordinate of thereference point P relative to the X coordinate of the image center C andthe displacement of the Y coordinate of the reference point P relativeto the Y coordinate of the image center C, or the second featureparameter is the projection of the displacement vector of the referencepoint P relative to the image center C on the reference line B, but thisembodiment is not limited thereto.

In another embodiment, the detection module 25 further determines afirst angle α between the tangential direction of the first lane stripeL1 and a direction V vertical to the reference line B, and determines athird feature parameter (referred to as test parameter) according to thefirst angle α. The detection module 25 further determines a second angleβ between the tangential direction of the second lane stripe L2 and thedirection V vertical to the reference line B, and determines a fourthfeature parameter (referred to as test parameter) according to thesecond angle β.

In this embodiment, the determination module 23 includes aclassification unit 231, a model generating unit 232, a comparing unit233 and a searching unit 234. The classification unit 231 determines themodel category of the lane stripe image 31 according to the firstfeature parameter, the second feature parameter, the third featureparameter, the fourth feature parameter or a combination thereof. Inthis embodiment, the classification unit 231 has a plurality of modelcategories, and each of the model categories corresponds to a pluralityof feature parameter ranges. The classification unit 231 decides themodel category of the lane stripe image 31 according to the featureparameter range within which the first feature parameter is, the featureparameter range within which the second feature parameter is, thefeature parameter range within which the third feature parameter is, andthe feature parameter range within which the fourth feature parameteris.

In this embodiment, each model category defines at least one set ofmodel parameter ranges, and a model parameter range is, for example, therange of the width of a lane model, the range of the curvature of a lanemodel, the horizontal distance between the left lane stripe and theimage center, the front rake angle of a lane model, the angle betweenthe middle line of a lane model and the middle line of the image, thevertical distance between the image capturing module 24 and the groundduring capturing images for the simulation of lane models, the focallength parameter of the image capturing module 24 during capturingimages for the simulation of lane models, or other parameters related toa lane model. When the classification unit 231 decides the modelcategory of the lane stripe image 31 according to the first featureparameter, the second feature parameter, the third feature parameter,the fourth feature parameter or a combination thereof, a set of modelparameter ranges defined by the model category can be obtained.

The model generating unit 232 receives a set of model parameter rangesrelated to the lane stripe image 31, and respectively generates aplurality of lane stripe models according to each model parameter ineach model parameter range, such as lane stripe models 40 a^ 40 c, asshown in FIGS. 4A to 4C. In an embodiment, the model generating unit 232sets one of the model parameters in each model parameter range as areplacement into a model generating formula, to generate a lane stripemodel.

The model generating formula is, for example:

${u_{l} = {f^{u}\left( {\frac{\theta}{\cos\;\varphi} + {\frac{\cos\;\varphi}{{Hf}^{v}}{x_{c}\left( {v_{l} + {f^{v}\tan\;\varphi}} \right)}} + \frac{f^{v}{{HC}_{0}/\cos^{3}}\varphi}{4\left( {v_{l} + {f^{v}\tan\;\varphi}} \right)}} \right)}},\mspace{14mu}{and}$${u_{r} = {f^{u}\left( {\frac{\theta}{\cos\;\varphi} + {\frac{\cos\;\varphi}{{Hf}^{v}}\left( {x_{c} - L} \right)\left( {v_{r} + {f^{v}\tan\;\varphi}} \right)} + \frac{f^{v}{{HC}_{0}/\cos^{3}}\varphi}{4\left( {v_{r} + {f^{v}\tan\;\varphi}} \right)}} \right)}},$wherein L represents the width of a lane model, C0 represents thecurvature of a lane model, xc represents the horizontal distance betweenthe left lane stripe and the image center, φ represents the front rakeangle of a lane model, θ represents the angle between the meddle line ofa lane model and the meddle line of the image, H represents the verticaldistance between the image capturing module 24 and the ground duringcapturing images for the simulation of lane models, and fu and fvrepresent the focal length parameters of the image capturing module 24during capturing images for the simulation of lane models.

As an example based on one of the model parameter ranges, the lanecurvature range related to the model category of the lane stripe image31 is from 0.25 to 0.28, and the model generating unit 232 replaces C₀with 0.25, 0.26, 0.27 and 0.28, for an example, to generate a pluralityof lane stripe models, respectively. In other words, the modelgenerating unit 232 respectively sets combinations of different modelparameters as replacements into the model generating formula, so as togenerate a plurality of lane stripe models of each model category.

In another embodiment, the model generating formula is, for example:

${{u_{l} - u_{0}} = {{a_{l}\left( {v - v_{0}} \right)} + \frac{b}{v - v_{0}}}},\mspace{14mu}{and}$${{u_{r} - u_{0}} = {{a_{r}\left( {v - v_{0}} \right)} + \frac{b}{v - v_{0}}}},$wherein (ul,v) represents the coordinate of the first lane stripe,(ur,v) represents the coordinate of the second lane stripe, (u0,v0)represents the coordinate of a reference point, al represents a tangentslope of the first lane stripe, ar represents a tangent slope of thesecond lane stripe, and b represents a parameter related to the radiusof the lane curvature.

In yet another embodiment, the model generating formula is, for example:u _(l) =a _(l) v ² +b _(l) v+c _(l), andu _(r) =a _(r) v ² +b _(r) v+c _(r),wherein (ul,v) represents the coordinate of the first lane stripe,(ur,v) represents the coordinate of the second lane stripe, and a, b andc are secondary curve parameters.

In addition to the above model generating formulas, other suitable modelgenerating formulas may be used in the model generating unit 232 in thisembodiment, and this embodiment is not limited thereabove.

The comparing unit 233 receives the lane stripe image 31 from the noiseprocessing module 26 and receives the lane stripe model in the modelcategory corresponding to the lane stripe image 31 from the modelgenerating unit 232. The comparing unit 233 compares the lane stripeimage 31 with each lane stripe model in the model category and selectsone lane stripe model according to the difference between the lanestripe image 31 and each lane stripe model.

In an embodiment, as shown in FIG. 5, the comparing unit 233superimposes the lane stripe model 40 c on the lane stripe image 31 forcomparison and employs a scan block 41 to scan a lane stripe image 40along the lane stripe model 40 c. The scan block 41 is exemplified asshown in the lower enlarged part of FIG. 5, and includes a plurality ofscanning girds arranged in parallel, and the comparing unit 233 detectsthe difference between the lane stripe in the lane stripe image 31 andthe lane stripe model 40 c by, for example, moving the scanning girds ofthe scan block 41 along the lane stripe model 40 c to detect thedifference between the lane stripe model 40 c and the lane stripe in thelane stripe image 31, such as the number of scanning girds xi betweenthe lane stripe model 40 c and the lane stripe in the lane stripe image31. Then, the comparing unit 233 determines the difference between thelane stripe image 31 and each lane stripe model in the model category bya sum of absolute difference method, a root mean square (RMS) method orother suitable calculation methods.

The RMS equation is, for example:

${x_{rms} = \sqrt{\frac{1}{n}\left( {x_{1}^{2} + x_{2}^{2} + x_{3}^{2} + \ldots + x_{n}^{2}} \right)}},$wherein x1˜xn are differences between the lane stripe model 40 c and thelane stripe of the lane stripe image 31, detected by the scan block 41,respectively. The comparing unit 233 selects one of the lane stripemodels in the model category according to the difference between thelane stripe image 31 and each lane stripe model in the model category.As an example, the selected lane stripe model has the smallestdifference with the lane stripe image 31.

The searching unit 234 searches for a set of actual lane parameters in aparameter lookup table according to the lane stripe model selected bythe comparing unit 233. The parameter lookup table records the actuallane parameter of each lane stripe model. The actual lane parameter isrelated to an environmental message of one or more real lanes, such asthe width of a real lane, the curvature of a real lane, the actualdistance between a left lane stripe and a vehicle, the front rake angleof a vehicle, the angle between the meddle line of a lane and a meddleline of a vehicle, the distance between the image capturing module 24and the ground, the focal length parameter of the image capturing module24, or other parameters of one or more real lanes.

In this embodiment, the actual lane parameter looked up by the searchingunit 234 can be used as the real situation of one or more lanes while alane stripe image is being captured, and this actual lane parameter isprovided to, for example, an automatic driving assistance system fordriving determination. Specifically, the image analysis device 20 sortslane stripe images and then compares each lane stripe image with a lanestripe model related to the category of the lane stripe image, so it maybe achieved to more simplify the complexity of real-time imagedetermination and bring the potency of automatic driving assistancesystems into full play.

In the previous embodiment, it is only for an exemplary description thatthe projection computing module 21 clockwisely rotates the lane stripeimage 31 to a variety of preset angles, which have an interval of 15°therebetween, from 0° to 90°; and another embodiment may be contemplatedthat the projection computing module 21 rotates the lane stripe image 31to a variety of preset angles, which have an interval of 15° or 20°therebetween, from −90° to 90°, and the disclosure is not limitedthereto.

Please refer to FIG. 6. FIG. 6 is a block diagram of an image analysisdevice according to yet another embodiment of the disclosure. As shownin FIG. 6, an image analysis device 50 includes a projection computingmodule 51, a processing module 52, a determination module 53, an imagecapturing module 54, a detection module 55, a noise processing module 56and a parameter table establishing module 57. The processing module 52is electrically connected to the projection computing module 51 and thedetermination module 53, the detection module 55 is electricallyconnected to the image capturing module 54, the noise processing module56 and the determination module 53, and the noise processing module 56is electrically connected to the projection computing module 51. Thedetermination module 53 includes a classification unit 531, a modelgenerating unit 532, a comparing unit 533 and a searching unit 534. Thecomparing unit 533 is electrically connected to the noise processingmodule 56, the model generating unit 532 and a searching unit 534, andthe classification unit 531 is electrically connected to the modelgenerating unit 532, the processing module 52 and the detection module55. The parameter table establishing module 57 is electrically connectedto the comparing unit 533 and the searching unit 534.

The projection computing module 51, the processing module 52, thedetermination module 53, the image capturing module 54, the detectionmodule 55 and the noise processing module 56 are substantially the sameas the previous embodiment. Between the previous embodiment and thisembodiment, there is a difference that in this embodiment, the parameterlookup table, which is used by the searching unit 534 to search data, isestablished by collecting data by the parameter table establishingmodule 57. In this embodiment, the image capturing module 54 captures aplurality of test images in a test environment, and then the detectionmodule 55 and the noise processing module 56 perform image process ontoeach test image to remove the content of the test image except one ormore lane stripes. The environmental message of the test environmentindicates at least one actual lane parameter of each test image. Forexample, the environmental message of the test environment is the width,curvature or other information of a lane in measurement. When the imagecapturing module 54 captures test images at a variety of image capturingpositions in a test environment, a different image capturing positioncorresponds to a different actual lane parameter. In other words, theimage capturing module 54 provides a variety of actual lane parametersfor a variety of image capturing positions in a test environment, so atest image captured at a variety of image capturing positions by theimage capturing module 54 corresponds to a set of actual lane parametersrelated to the image capturing position.

The projection computing module 51 receives the test image and rotatesthe received test image relative to a reference point as a center ofrotation in the received test image to a plurality of preset angles.Whenever the test image is rotated to one of the preset angles, theprojection computing module 51 defines a plurality of preset sectionsonto the test image and determines the characteristic value of the testimage in each of the preset sections. The processing module 52determines the first feature parameter of the test image according tothe characteristic value in each of the preset sections whenever theprojection computing module 51 recognizes the test image in at least oneof the default directions. In another embodiment, the detection module55 determines the image center of each test image and determines thesecond feature parameter of each test image according to thedisplacement of a reference point in the test image relative to theimage center. The detection module 55 determines the third featureparameter of each test image according to the first angle between thetangential direction of the first lane stripe and the direction verticalto the reference line. The detection module 45 determines the fourthfeature parameter of each test image according to the second anglebetween the tangential direction of the second lane stripe and thedirection vertical to the reference line.

The classification unit 531 determines the model category of each testimage according to the first feature parameter, the second featureparameter, the third feature parameter, the fourth feature parameter, ora combination thereof. In practice, the classification unit 531 providesa plurality of model categories, and each of the model categoriescorresponds to a plurality of feature parameter ranges. Thedetermination module 53 decides the model category of each test imageaccording to the feature parameter ranges which the first featureparameter, the second feature parameter, the third feature parameter andthe fourth feature parameter are respectively within.

The model generating unit 532 receives a set of model parameter rangesrelated to the test image, and respectively generates a plurality oflane stripe models according to each model parameter in each modelparameter range. The manner used by the model generating unit 532 togenerate the lane stripe models can be referred to the previousembodiment or be other suitable manners, and there are no more relateddescriptions.

Then, the comparing unit 533 receives each test image from the noiseprocessing module 56 and the lane stripe models related to the modelcategory of the test image from the model generating unit 532. Thecomparing unit 533 compares the test image with each lane stripe modelrelated to the model category, and selects one of the lane stripe modelsfor each test image according to the difference between the test imageand each lane stripe model. The comparing unit 533 is not limited tosuperimpose the test image and a lane stripe model together fordetermination as described in the previous embodiment.

The comparing unit 533 selects one of the lane stripe models related tothe model category according to the difference between the test imageand each lane stripe model in the model category. The selected lanestripe model is a lane stripe model having a relatively-small differencewith the test image.

The parameter table establishing module 57 establishes a parameterlookup table according to the lane stripe model selected by thecomparing unit 533 and the actual lane parameter of the test image. Inother words, the image analysis device 50 captures a plurality of testimages in a test environment and uses known actual lane parametersrelated to the test environment and the lane stripe models of each testimage to establish a relationship table so that the searching unit 534can look up a set of actual lane parameters related to a lane stripeimage in the parameter lookup table and provide them to an automaticdriving assistance system for the determination basis of automaticdriving.

In this embodiment, the parameter table establishing module 57 permitsthe image analysis device 50 to establish complicated informationdetermination. On the other hand, when each image analysis device isdisposed in a variety of environments, such as the automatic drivingassistance system of a variety of vehicle models, a variety of vehiclemodels have their respective determination bases in the same physicalenvironment having lane stripes. The parameter table establishing module57 can establish a variety of parameter lookup tables for a variety ofvehicle models, so the image analysis device may be applied more widely.

For a more clear description of the lane stripe analysis method, pleaserefer to FIG. 7. FIG. 7 is a flow chart of an analysis method of lanestripe images according to an embodiment of the disclosure. As shown inFIG. 7, in step S601, a reference point is set as a center to recognizea lane stripe image in a plurality of default directions. In step S602,whenever the lane stripe image is recognized in one of the defaultdirections, a plurality of preset sections, which is arranged inparallel in a reference line vertical to the default direction, isdefined onto the lane stripe image. In step S603, the characteristicvalue of the lane stripe image in each of the preset sections isdetermined. In step S604, a first feature parameter is determinedaccording to the characteristic values of the lane stripe image in thepreset sections when the lane stripe image is recognized in at least oneof the default directions. In step S605, at least one actual laneparameter corresponding to the lane stripe image is determined accordingto at least the first feature parameter. The actual lane parameter isrelated to an environmental message of one or more real lanes. Theforegoing lane stripe analysis method has been described in detail inthe previous embodiments, and there are no more descriptions hereafter.

Next, refer to FIG. 1 and FIG. 8. FIG. 8 is a flow chart of anon-transitory computer readable medium executable to a lane stripedetection device according to an embodiment of the disclosure. As shownin FIG. 8, the foregoing analysis method can be applied to automaticdriving assistance systems, a vehicle's computer system, automotivecomputers, the image analysis device 10 or other suitable electronicdevices in practice. In other words, the lane stripe analysis method inthis embodiment can be programmed by one or more computer programlanguages to produce a non-transitory computer readable medium, and thenon-transitory computer readable medium will be read by the imageanalysis device 10 to analyze a variety of lane stripe images. When theimage analysis device 10 executes a program stored in the non-transitorycomputer readable medium to perform the analysis method of lane stripeimages, as described in the steps in FIG. 7. In step S701, theprojection computing module 11 is driven to set a reference point as acenter to recognize a lane stripe image in a plurality of defaultdirections. In step S702, the projection computing module 11 is drivento define a plurality of preset sections onto the lane stripe imagewhenever recognizing the lane stripe image in one of the defaultdirections. In step S703, the projection computing module 11 is drivento determine the characteristic value of the lane stripe image in eachof the preset sections. In step S704, the processing module 12 is drivento determine a first feature parameter. In step S704, the determinationmodule 13 is driven to determine at least one actual lane parameter ofthe lane stripe image according to at least the first feature parameter.In view of the foregoing embodiment, a person of ordinary skill in theart can understand how the processor reads the executable content fromthe non-transitory computer readable medium, and thus, there are no moredescriptions hereafter.

To sum up, the disclosure provides an image analysis device, an analysismethod and a non-transitory computer readable medium thereof, to analyzethe characteristic value of a lane stripe image in each of the presetsections while rotating the lane stripe image to a variety of presetangles, and determine the model category of the lane stripe imageaccording to the characteristic values. Therefore, the disclosure mayachieve the lower complexity of calculation for the analysis of lanestripes, resulting in the lower cost of automatic driving assistancesystems.

What is claimed is:
 1. An analysis method of lane stripe images,comprising: setting a reference point as a center to recognize the lanestripe image in a plurality of default directions; defining a pluralityof preset sections, which is arranged in parallel in a reference linevertical to the default direction, of the lane stripe image whenever thelane stripe image is being recognized in one of the default directions;determining a characteristic value of the lane stripe image in each ofthe preset sections; determining a first feature parameter according tothe characteristic values of the lane stripe image in the presetsections when the lane stripe image is being recognized in at least oneof the default directions; and determining at least one actual laneparameter of the lane stripe image, which is associated with anenvironmental message of one or more real lanes, according to at leastthe first feature parameter, wherein said at least one actual laneparameter is determined based on a plurality of image recognitionresults respectively for the plurality of default directions.
 2. Theanalysis method according to claim 1, wherein recognizing the lanestripe image in each of the default directions comprises: rotating thelane stripe image to a plurality of preset angles relative to thereference point as a center of rotation, wherein when the lane stripeimage is rotated to one of the preset angles, the lane stripe image isrecognized.
 3. The analysis method according to claim 1, whereindetermining the actual lane parameter of the lane stripe imagecomprises: determining a model category of the lane stripe image, whichhas a model parameter range comprising a plurality of model parameters,according to at least the first feature parameter; respectivelygenerating a plurality of lane stripe models according to each of themodel parameters in the model parameter range; comparing the lane stripeimage with the lane stripe models; selecting one of the lane stripemodels according to a difference between the lane stripe image and eachof the lane stripe models; and searching for the actual lane parameterrelated to the selected lane stripe model in a parameter lookup tablewhich records the actual lane parameters respectively corresponding tothe lane stripe models.
 4. The analysis method according to claim 3,further comprising: capturing a roadway image by an image capturingmodule; recognizing a first lane stripe and a second lane stripe in theroadway image; determining the reference point, which is located in atangential direction of the first lane stripe and a tangential directionof the second lane stripe, according to the first lane stripe and thesecond lane stripe in the roadway image; and generating the lane stripeimage by filtering noises in the roadway image according to thereference point.
 5. The analysis method according to claim 4, furthercomprising: determining an image center of the roadway image; anddetermining a second feature parameter according to a displacement ofthe reference point relative to the image center, wherein the secondfeature parameter is also used to determine the model category of thelane stripe image.
 6. The analysis method according to claim 5, furthercomprising: determining a first angle between the tangential directionof the first lane stripe and a direction vertical to the reference line;determining a third feature parameter according to the first angle;determining a second angle between the tangential direction of thesecond lane stripe and the direction vertical to the reference line; anddetermining a fourth feature parameter according to the second angle,wherein the third feature parameter and the fourth feature parameter arefurther used to determine the model category of the lane stripe image.7. The analysis method according to claim 4, further comprising:capturing a plurality of test images in a test environment by the imagecapturing module, and an environmental message of the test environmentrecording the at least one actual lane parameter of each of the testimages; determining the lane stripe model of each of the test images;and establishing the parameter lookup table according to the lane stripemodel of each of the test images and the at least one actual laneparameter.
 8. The analysis method according to claim 7, whereindetermining the lane stripe model of each of the test images comprises:rotating one of the test images to a plurality of preset angles relativeto the reference point as a center of rotation; defining the presetsections onto the test image when the test image is rotated to one ofthe preset angles; determining a test parameter of the test imageaccording to the characteristic values of the lane stripe image in thepreset sections when the test image is rotated to one of the presetangles; determining the model category of the test image according to atleast the test parameter; comparing the test image with the lane stripemodels; and selecting the lane stripe model of the test image accordingto a difference between the test image and each of the lane stripemodels.
 9. An image analysis device, comprising: a projection computingmodule configured to set a reference point as a center to recognize alane stripe image in a plurality of default directions, define aplurality of preset sections, which is arranged in parallel in areference line that is vertical to the default direction, of the lanestripe image, and determine a characteristic value of the lane stripeimage in each of the preset sections whenever the lane stripe image isrecognized in one of the default directions; a processing moduleelectrically connected to the projection computing module and configuredto determine a first feature parameter according to the characteristicvalues of the lane stripe image in the preset sections when the lanestripe image is recognized in at least one of the default directions;and a determination module electrically connected to the processingmodule and configured to determine at least one actual lane parameter ofthe lane stripe image, which is associated with a environmental messageof one or more real lanes, according to at least the first featureparameter, wherein said at least one actual lane parameter is determinedbased on a plurality of image recognition results respectively for theplurality of default directions.
 10. The image analysis device accordingto claim 9, wherein the projection computing module further rotates thelane stripe image to a plurality of preset angles relative to thereference point as a center of rotation, and when the lane stripe imageis rotated to one of the preset angles, the projection computing modulerecognizes the lane stripe image.
 11. The image analysis deviceaccording to claim 9, wherein the determination module comprises: aclassification unit configured to determine a model category of the lanestripe image, which has a model parameter range comprising a pluralityof model parameters, according to at least the first feature parameter;a model generating unit electrically connected to the classificationunit and configured to respectively generate a plurality of lane stripemodels according to each of the model parameters in the model parameterrange; a comparing unit electrically connected to the model generatingunit and configured to compare the lane stripe image with the lanestripe models and select one of the lane stripe models according to adifference between the lane stripe image and each of the lane stripemodels; and a searching unit configured to search for the actual laneparameter of the lane stripe model in a parameter lookup table accordingto the lane stripe model selected by the comparing unit, and theparameter lookup table recording the actual lane parameter of each ofthe lane stripe models.
 12. The image analysis device according to claim11, further comprising: an image capturing module configured to capturea roadway image; a detection module electrically connected to the imagecapturing module and configured to recognize at least a first lanestripe and a second lane stripe in the roadway image and determine thereference point according to the first lane stripe and the second lanestripe in the roadway image, and the reference point being located in atangential direction of the first lane stripe and a tangential directionof the second lane stripe; and a noise processing module electricallyconnected to the detection module and the projection computing moduleand configured to generate and output the lane stripe image to theprojection computing module by filtering noises in the roadway imageaccording to the reference point recognized by the detection module. 13.The image analysis device according to claim 12, wherein the detectionmodule further determines an image center of the roadway image anddetermines a second feature parameter according to a displacement of thereference point relative to the image center, and the classificationunit further determines the model category of the lane stripe imageaccording to the second feature parameter.
 14. The image analysis deviceaccording to claim 13, wherein the detection module further determines afirst angle between the tangential direction of the first lane stripeand a direction vertical to the reference line, and determines a thirdfeature parameter according to the first angle; the detection modulefurther determines a second angle between the tangential direction ofthe second lane stripe and the direction vertical to the reference line,and determines a fourth feature parameter according to the second angle;and the classification unit further determines the model category of thelane stripe image according to the third feature parameter and thefourth feature parameter.
 15. The image analysis device according toclaim 12, wherein the image capturing module captures a plurality oftest images in a test environment, an environmental message of the testenvironment record the at least one actual lane parameter of each of thetest images, and the projection computing module, the processing moduleand the determination module determine the lane stripe model of each ofthe test images, and the image analysis device further comprises: aparameter table establishing module configured to establish theparameter lookup table according to the lane stripe model of each of thetest images and the at least one actual lane parameter.
 16. The imageanalysis device according to claim 15, wherein the projection computingmodule rotates one of the test images to a plurality of preset anglesrelative to the reference point as a center of rotation, and when thetest image is rotated to one of the preset angles, the projectioncomputing module defines the preset sections onto the test image anddetermines the characteristic values of the lane stripe image in thepreset sections, the processing module determines a test parameter ofthe test image according to the characteristic values of the lane stripeimage in the preset sections, the classification unit determines themodel category of the test image according to at least the testparameter, and the comparing unit compares the test image with the lanestripe models and selects the lane stripe model of the test imageaccording to a difference between the test image and each of the lanestripe models.
 17. A non-transitory computer readable medium comprisinga program for being executed by an electronic device, which comprises aprojection computing module, a processing module and a determinationmodule, to perform steps of: driving the projection computing module toset a reference point as a center to recognize a lane stripe image in aplurality of default directions; driving the projection computing moduleto define a plurality of preset sections, which is arranged in parallelin a reference line vertical to the default direction, of the lanestripe image and determine a characteristic value of the lane stripeimage in each of the preset sections whenever recognizing the lanestripe image in one of the default directions; driving the processingmodule to determine a first feature parameter related to thecharacteristic values of the lane stripe image in the preset sectionswhen the lane stripe image is recognized in at least one of the defaultdirections; and driving the determination module to determine at leastone actual lane parameter of the lane stripe image according to at leastthe first feature parameter, and the actual lane parameter being relatedto an environmental message of one or more real lanes, wherein said atleast one actual lane parameter is determined based on a plurality ofimage recognition results respectively for the plurality of defaultdirections.
 18. The non-transitory computer readable medium according toclaim 17, wherein the step of driving the projection computing module toset the reference point to recognize the lane stripe image in thedefault directions comprises: driving the projection computing module torotate the lane stripe image to a plurality of preset angles relative tothe reference point as a center of rotation, wherein when the lanestripe image is rotated to one of the preset angles, the projectioncomputing module recognizes the lane stripe image.
 19. Thenon-transitory computer readable medium according to claim 17, whereinthe determination module further comprises a classification unit, amodel generating unit, a comparing unit and a searching unit, and thenon-transitory computer readable medium executable to the electronicdevice to further perform steps of: driving the classification unit todetermine a model category of the lane stripe image, which has a modelparameter range comprising a plurality of model parameters, according toat least the first feature parameter; driving the model generating unitto respectively generate a plurality of lane stripe models according toeach of the model parameters in the model parameter range; driving thecomparing unit to compare the lane stripe image with the lane stripemodels and select one of the lane stripe models according to adifference between the lane stripe image and each of the lane stripemodels; and driving the searching unit to search for the actual laneparameter of the selected lane stripe model in a parameter lookup tablethat records the actual lane parameter of each of the lane stripemodels.
 20. The non-transitory computer readable medium according toclaim 19, wherein the electronic device further comprises an imagecapturing module, a detection module and a noise processing module, andthe non-transitory computer readable medium is executable to theelectronic device to further perform steps of: driving the imagecapturing module to capture a roadway image; driving the detectionmodule to recognize a first lane stripe and a second lane stripe in theroadway image; driving the detection module to determine the referencepoint, which is located in a tangential direction of the first lanestripe and a tangential direction of the second lane stripe, accordingto the first lane stripe and the second lane stripe in the roadwayimage; and driving the noise processing module to generate and outputthe lane stripe image to the projection computing module by filteringnoises in the roadway image according to the reference point recognizedby the detection module.
 21. The non-transitory computer readable mediumaccording to claim 20, wherein the electronic device executes theprogram in the non-transitory computer readable medium to furtherperform steps of: driving the detection module to determine an imagecenter of the roadway image; driving the detection module to determine asecond feature parameter according to a displacement of the referencepoint relative to the image center; and driving the classification unitto determine the model category of the lane stripe image furtheraccording to the second feature parameter.
 22. The non-transitorycomputer readable medium according to claim 21, wherein the electronicdevice executes the program in the non-transitory computer readablemedium to further perform steps of: driving the detection module todetermine a first angle between the tangential direction of the firstlane stripe and a direction vertical to the reference line, anddetermine a third feature parameter according to the first angle;driving the detection module to determine a second angle between thetangential direction of the second lane stripe and the directionvertical to the reference line, and determine a fourth feature parameteraccording to the second angle; and driving the classification unit todetermine the model category of the lane stripe image further accordingto the third feature parameter and the fourth feature parameter.
 23. Thenon-transitory computer readable medium according to claim 20, whereinthe electronic device further comprises a parameter table establishingmodule, and the electronic device executes the program in thenon-transitory computer readable medium to further perform steps of:driving the image capturing module to capture a plurality of test imagesin a test environment, and an environmental message of the testenvironment recording the at least one actual lane parameter of each ofthe test images; driving the projection computing module, the processingmodule and the determination module to determine the lane stripe modelof each of the test images; and driving the parameter table establishingmodule to establish the parameter lookup table according to the lanestripe model of each of the test images and the at least one actual laneparameter.
 24. The non-transitory computer readable medium according toclaim 23, wherein the electronic device executes the program in thenon-transitory computer readable medium to further perform steps of:driving the projection computing module to rotate one of the test imagesto a plurality of preset angles relative to the reference point as acenter of rotation; driving the projection computing module to definethe preset sections onto the test image and determine the characteristicvalues of the lane stripe image in the preset sections when the testimage is rotated to one of the preset angles; driving the processingmodule to determine a test parameter of the test image according to thecharacteristic values of the lane stripe image in the preset sections;driving the classification unit to determine the model category of thetest image according to at least the test parameter; and driving thecomparing unit to compare the test image with the lane stripe models andselect the lane stripe model of the test image according to a differencebetween the test image and each of the lane stripe models.